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electricsheepasia/asia-who-specialist-medical-practitioners

Specialist medical practitioners (number) | Asia (WHO GHO) ๐ŸŒ 394 observations ยท 31 Asia countries ยท 1980โ€“2024 ยท Repackaged by Electric Sheep Asia TL;DR This dataset contains 394 observations of Specialist medical practitioners (number) data across 31 Asia countries, spanning 1980โ€“2024, covering 1 distinct indicators. About the source Source: WHO Global Health Observatory Publisher: World Health Organization License: cc-by-4.0 Topic:โ€ฆ See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-who-specialist-medical-practitioners.

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Dataset Card

Specialist medical practitioners (number) | Asia (WHO GHO)

๐ŸŒ 394 observations ยท 31 Asia countries ยท 1980โ€“2024 ยท Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years indicators license

TL;DR

This dataset contains 394 observations of Specialist medical practitioners (number) data across 31 Asia countries, spanning 1980โ€“2024, covering 1 distinct indicators.

About the source

Geographic coverage

31 Asia countries ยท top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
KAZ4519802024
TUR4419802023
AZE3319902022
GEO2719962022
ISR2520002024
TKM2520002024
ARM2320002022
KOR2020042023
CYP1819862023
UZB1620082023
SGP1620082023
LAO1320052022
LKA1220112023
OMN1020142024
IDN1020122024
...16 more countries

Indicators (sample)

  • โ€”HWF_0004

Schema

ColumnTypeDescriptionExample
indicator_codeobjectโ€”HWF_0004
country_iso3objectโ€”AFG
who_regionobjectโ€”EMR
yearint64โ€”2023
value_numericfloat64โ€”5056.0
value_lowobjectโ€”โ€”
value_highobjectโ€”โ€”
value_displayobjectโ€”5056
last_updatedobjectโ€”2026-01-23T13:29:16.4+01:00

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-who-specialist-medical-practitioners")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
indonesia = df[df["country_iso3"] == "IDN"]

Time-series for a single indicator

python
sample = (df[df["indicator_code"] == "HWF_0004"]
          .sort_values("year"))
sample.plot(x="year", y="value_numeric", title="HWF_0004")

Pivot to country ร— year matrix

python
matrix = (df[df["indicator_code"] == "HWF_0004"]
          .pivot_table(index="year", columns="country_iso3", values="value_numeric"))
print(matrix.tail())

Citation

bibtex
@misc{asia_who_specialist_medical_practitioners_2024,
  title        = {Specialist medical practitioners (number) | Asia (WHO GHO)},
  author       = {World Health Organization},
  year         = {2024},
  url          = {https://www.who.int/data/gho},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-who-specialist-medical-practitioners}}
}

License

Released under cc-by-4.0.

Original data ยฉ World Health Organization. When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.

About Electric Sheep

Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepasia


Provenance: ingested 2026-05-29 via the Electric Sheep pipeline. Source URL: https://www.who.int/data/gho